Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory440.0 B

Variable types

Text1
DateTime2
Numeric9
Categorical9

Alerts

auto_renew is highly overall correlated with clusterHigh correlation
cluster is highly overall correlated with auto_renew and 1 other fieldsHigh correlation
discount_used is highly overall correlated with cluster and 1 other fieldsHigh correlation
interaction_score is highly overall correlated with monthly_feeHigh correlation
logins_per_month is highly overall correlated with num_logins_last_30d and 3 other fieldsHigh correlation
monthly_fee is highly overall correlated with interaction_scoreHigh correlation
num_logins_last_30d is highly overall correlated with logins_per_month and 1 other fieldsHigh correlation
pca1 is highly overall correlated with discount_usedHigh correlation
pca2 is highly overall correlated with logins_per_month and 2 other fieldsHigh correlation
support_tickets is highly overall correlated with pca2High correlation
tenure_months is highly overall correlated with logins_per_month and 1 other fieldsHigh correlation
tenure_weeks is highly overall correlated with logins_per_month and 1 other fieldsHigh correlation
customer_id has unique values Unique
signup_date has unique values Unique
pca1 has unique values Unique
pca2 has unique values Unique
support_tickets has 2268 (22.7%) zeros Zeros
interaction_score has 1882 (18.8%) zeros Zeros

Reproduction

Analysis started2025-04-14 16:51:56.093185
Analysis finished2025-04-14 16:52:02.930033
Duration6.84 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.7 KiB
2025-04-14T22:22:03.176180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowCUST00000
2nd rowCUST00001
3rd rowCUST00002
4th rowCUST00003
5th rowCUST00004
ValueCountFrequency (%)
cust00000 1
 
< 0.1%
cust00008 1
 
< 0.1%
cust00017 1
 
< 0.1%
cust00002 1
 
< 0.1%
cust00003 1
 
< 0.1%
cust00004 1
 
< 0.1%
cust00005 1
 
< 0.1%
cust00006 1
 
< 0.1%
cust00007 1
 
< 0.1%
cust00009 1
 
< 0.1%
Other values (9990) 9990
99.9%
2025-04-14T22:22:03.528738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14000
15.6%
C 10000
11.1%
U 10000
11.1%
S 10000
11.1%
T 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
C 10000
11.1%
U 10000
11.1%
S 10000
11.1%
T 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
C 10000
11.1%
U 10000
11.1%
S 10000
11.1%
T 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
C 10000
11.1%
U 10000
11.1%
S 10000
11.1%
T 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

signup_date
Date

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2019-01-01 00:00:00
Maximum2023-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-14T22:22:03.634303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:03.734306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct9998
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2019-02-12 13:01:59.891989
Maximum2025-06-02 00:59:37.317731
Invalid dates0
Invalid dates (%)0.0%
2025-04-14T22:22:03.829829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:03.933691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tenure_months
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.4832
Minimum15
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:04.070666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q127
median39
Q352
95-th percentile61
Maximum64
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.065771
Coefficient of variation (CV)0.35624698
Kurtosis-1.1981751
Mean39.4832
Median Absolute Deviation (MAD)12
Skewness-2.4284123 × 10-5
Sum394832
Variance197.8459
MonotonicityDecreasing
2025-04-14T22:22:04.160576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 206
 
2.1%
24 206
 
2.1%
33 206
 
2.1%
43 206
 
2.1%
30 206
 
2.1%
46 206
 
2.1%
27 206
 
2.1%
49 206
 
2.1%
52 206
 
2.1%
37 206
 
2.1%
Other values (40) 7940
79.4%
ValueCountFrequency (%)
15 76
 
0.8%
16 205
2.1%
17 205
2.1%
18 206
2.1%
19 205
2.1%
20 205
2.1%
21 206
2.1%
22 205
2.1%
23 205
2.1%
24 206
2.1%
ValueCountFrequency (%)
64 69
 
0.7%
63 205
2.1%
62 206
2.1%
61 205
2.1%
60 205
2.1%
59 206
2.1%
58 205
2.1%
57 205
2.1%
56 205
2.1%
55 206
2.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size611.2 KiB
Basic
4079 
Premium
3890 
Free
2031 

Length

Max length7
Median length5
Mean length5.5749
Min length4

Characters and Unicode

Total characters55749
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBasic
2nd rowPremium
3rd rowPremium
4th rowPremium
5th rowPremium

Common Values

ValueCountFrequency (%)
Basic 4079
40.8%
Premium 3890
38.9%
Free 2031
20.3%

Length

2025-04-14T22:22:04.243737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:04.316720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
basic 4079
40.8%
premium 3890
38.9%
free 2031
20.3%

Most occurring characters

ValueCountFrequency (%)
i 7969
14.3%
e 7952
14.3%
m 7780
14.0%
r 5921
10.6%
B 4079
7.3%
a 4079
7.3%
s 4079
7.3%
c 4079
7.3%
P 3890
7.0%
u 3890
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7969
14.3%
e 7952
14.3%
m 7780
14.0%
r 5921
10.6%
B 4079
7.3%
a 4079
7.3%
s 4079
7.3%
c 4079
7.3%
P 3890
7.0%
u 3890
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7969
14.3%
e 7952
14.3%
m 7780
14.0%
r 5921
10.6%
B 4079
7.3%
a 4079
7.3%
s 4079
7.3%
c 4079
7.3%
P 3890
7.0%
u 3890
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7969
14.3%
e 7952
14.3%
m 7780
14.0%
r 5921
10.6%
B 4079
7.3%
a 4079
7.3%
s 4079
7.3%
c 4079
7.3%
P 3890
7.0%
u 3890
7.0%

monthly_fee
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size574.5 KiB
30
4130 
10
3988 
0
1882 

Length

Max length2
Median length2
Mean length1.8118
Min length1

Characters and Unicode

Total characters18118
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row30
3rd row0
4th row30
5th row10

Common Values

ValueCountFrequency (%)
30 4130
41.3%
10 3988
39.9%
0 1882
18.8%

Length

2025-04-14T22:22:04.387345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:04.449395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
30 4130
41.3%
10 3988
39.9%
0 1882
18.8%

Most occurring characters

ValueCountFrequency (%)
0 10000
55.2%
3 4130
22.8%
1 3988
 
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10000
55.2%
3 4130
22.8%
1 3988
 
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10000
55.2%
3 4130
22.8%
1 3988
 
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10000
55.2%
3 4130
22.8%
1 3988
 
22.0%

auto_renew
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
6939 
0
3061 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6939
69.4%
0 3061
30.6%

Length

2025-04-14T22:22:04.516979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:04.576196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6939
69.4%
0 3061
30.6%

Most occurring characters

ValueCountFrequency (%)
1 6939
69.4%
0 3061
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6939
69.4%
0 3061
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6939
69.4%
0 3061
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6939
69.4%
0 3061
30.6%

payment_method
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size608.0 KiB
PayPal
2511 
Wallet
2501 
Card
2498 
Other
2490 

Length

Max length6
Median length6
Mean length5.2514
Min length4

Characters and Unicode

Total characters52514
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWallet
2nd rowPayPal
3rd rowWallet
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
PayPal 2511
25.1%
Wallet 2501
25.0%
Card 2498
25.0%
Other 2490
24.9%

Length

2025-04-14T22:22:04.649210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:04.721160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
paypal 2511
25.1%
wallet 2501
25.0%
card 2498
25.0%
other 2490
24.9%

Most occurring characters

ValueCountFrequency (%)
a 10021
19.1%
l 7513
14.3%
P 5022
9.6%
e 4991
9.5%
t 4991
9.5%
r 4988
9.5%
y 2511
 
4.8%
W 2501
 
4.8%
C 2498
 
4.8%
d 2498
 
4.8%
Other values (2) 4980
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10021
19.1%
l 7513
14.3%
P 5022
9.6%
e 4991
9.5%
t 4991
9.5%
r 4988
9.5%
y 2511
 
4.8%
W 2501
 
4.8%
C 2498
 
4.8%
d 2498
 
4.8%
Other values (2) 4980
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10021
19.1%
l 7513
14.3%
P 5022
9.6%
e 4991
9.5%
t 4991
9.5%
r 4988
9.5%
y 2511
 
4.8%
W 2501
 
4.8%
C 2498
 
4.8%
d 2498
 
4.8%
Other values (2) 4980
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10021
19.1%
l 7513
14.3%
P 5022
9.6%
e 4991
9.5%
t 4991
9.5%
r 4988
9.5%
y 2511
 
4.8%
W 2501
 
4.8%
C 2498
 
4.8%
d 2498
 
4.8%
Other values (2) 4980
9.5%

num_logins_last_30d
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.03
Minimum2
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:04.790282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q110
median12
Q314
95-th percentile18
Maximum27
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.481826
Coefficient of variation (CV)0.2894286
Kurtosis0.05046703
Mean12.03
Median Absolute Deviation (MAD)2
Skewness0.28543602
Sum120300
Variance12.123112
MonotonicityNot monotonic
2025-04-14T22:22:04.864980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
10 1112
11.1%
12 1100
11.0%
11 1087
10.9%
13 983
9.8%
14 930
9.3%
9 895
8.9%
15 767
7.7%
8 670
6.7%
16 542
 
5.4%
17 426
 
4.3%
Other values (16) 1488
14.9%
ValueCountFrequency (%)
2 7
 
0.1%
3 19
 
0.2%
4 45
 
0.4%
5 135
 
1.4%
6 248
 
2.5%
7 405
 
4.0%
8 670
6.7%
9 895
8.9%
10 1112
11.1%
11 1087
10.9%
ValueCountFrequency (%)
27 2
 
< 0.1%
26 2
 
< 0.1%
25 4
 
< 0.1%
24 8
 
0.1%
23 16
 
0.2%
22 40
 
0.4%
21 46
 
0.5%
20 86
 
0.9%
19 159
1.6%
18 266
2.7%

support_tickets
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4937
Minimum0
Maximum8
Zeros2268
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:04.932597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2295168
Coefficient of variation (CV)0.82313501
Kurtosis0.65018426
Mean1.4937
Median Absolute Deviation (MAD)1
Skewness0.82308794
Sum14937
Variance1.5117115
MonotonicityNot monotonic
2025-04-14T22:22:05.006424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3345
33.5%
2 2480
24.8%
0 2268
22.7%
3 1239
 
12.4%
4 478
 
4.8%
5 150
 
1.5%
6 29
 
0.3%
7 9
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 2268
22.7%
1 3345
33.5%
2 2480
24.8%
3 1239
 
12.4%
4 478
 
4.8%
5 150
 
1.5%
6 29
 
0.3%
7 9
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 9
 
0.1%
6 29
 
0.3%
5 150
 
1.5%
4 478
 
4.8%
3 1239
 
12.4%
2 2480
24.8%
1 3345
33.5%
0 2268
22.7%

feature_usage_score
Real number (ℝ)

Distinct4816
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.51902
Minimum0.22
Maximum86.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:05.092717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile6.27
Q116.05
median26.705
Q339.1325
95-th percentile57.89
Maximum86.96
Range86.74
Interquartile range (IQR)23.0825

Descriptive statistics

Standard deviation15.861775
Coefficient of variation (CV)0.55618234
Kurtosis-0.18220921
Mean28.51902
Median Absolute Deviation (MAD)11.365
Skewness0.56080079
Sum285190.2
Variance251.59592
MonotonicityNot monotonic
2025-04-14T22:22:05.295305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.84 10
 
0.1%
28.55 9
 
0.1%
22.49 9
 
0.1%
25.34 9
 
0.1%
19.15 8
 
0.1%
29.27 8
 
0.1%
24.6 8
 
0.1%
29.98 8
 
0.1%
14.59 7
 
0.1%
17.18 7
 
0.1%
Other values (4806) 9917
99.2%
ValueCountFrequency (%)
0.22 1
< 0.1%
0.31 1
< 0.1%
0.4 1
< 0.1%
0.49 1
< 0.1%
0.53 1
< 0.1%
0.61 1
< 0.1%
0.63 1
< 0.1%
0.65 1
< 0.1%
0.66 1
< 0.1%
0.78 1
< 0.1%
ValueCountFrequency (%)
86.96 1
< 0.1%
83.24 1
< 0.1%
82.36 1
< 0.1%
81.41 1
< 0.1%
81.33 1
< 0.1%
81.09 1
< 0.1%
81.01 1
< 0.1%
80.71 1
< 0.1%
80.59 1
< 0.1%
80.3 2
< 0.1%

discount_used
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
6871 
1
3129 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6871
68.7%
1 3129
31.3%

Length

2025-04-14T22:22:05.374584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:05.433351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6871
68.7%
1 3129
31.3%

Most occurring characters

ValueCountFrequency (%)
0 6871
68.7%
1 3129
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6871
68.7%
1 3129
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6871
68.7%
1 3129
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6871
68.7%
1 3129
31.3%

referral_channel
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size620.0 KiB
Paid
2576 
Partner
2524 
Referral
2460 
Organic
2440 

Length

Max length8
Median length7
Mean length6.4732
Min length4

Characters and Unicode

Total characters64732
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrganic
2nd rowReferral
3rd rowPartner
4th rowOrganic
5th rowPartner

Common Values

ValueCountFrequency (%)
Paid 2576
25.8%
Partner 2524
25.2%
Referral 2460
24.6%
Organic 2440
24.4%

Length

2025-04-14T22:22:05.508845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:05.583123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
paid 2576
25.8%
partner 2524
25.2%
referral 2460
24.6%
organic 2440
24.4%

Most occurring characters

ValueCountFrequency (%)
r 12408
19.2%
a 10000
15.4%
e 7444
11.5%
P 5100
7.9%
i 5016
7.7%
n 4964
7.7%
d 2576
 
4.0%
t 2524
 
3.9%
R 2460
 
3.8%
f 2460
 
3.8%
Other values (4) 9780
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 12408
19.2%
a 10000
15.4%
e 7444
11.5%
P 5100
7.9%
i 5016
7.7%
n 4964
7.7%
d 2576
 
4.0%
t 2524
 
3.9%
R 2460
 
3.8%
f 2460
 
3.8%
Other values (4) 9780
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 12408
19.2%
a 10000
15.4%
e 7444
11.5%
P 5100
7.9%
i 5016
7.7%
n 4964
7.7%
d 2576
 
4.0%
t 2524
 
3.9%
R 2460
 
3.8%
f 2460
 
3.8%
Other values (4) 9780
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 12408
19.2%
a 10000
15.4%
e 7444
11.5%
P 5100
7.9%
i 5016
7.7%
n 4964
7.7%
d 2576
 
4.0%
t 2524
 
3.9%
R 2460
 
3.8%
f 2460
 
3.8%
Other values (4) 9780
15.1%

country
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size602.4 KiB
UK
1720 
France
1719 
Germany
1694 
India
1649 
Canada
1613 

Length

Max length7
Median length6
Mean length4.6745
Min length2

Characters and Unicode

Total characters46745
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowFrance
3rd rowCanada
4th rowUK
5th rowIndia

Common Values

ValueCountFrequency (%)
UK 1720
17.2%
France 1719
17.2%
Germany 1694
16.9%
India 1649
16.5%
Canada 1613
16.1%
US 1605
16.1%

Length

2025-04-14T22:22:05.659242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:05.729451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
uk 1720
17.2%
france 1719
17.2%
germany 1694
16.9%
india 1649
16.5%
canada 1613
16.1%
us 1605
16.1%

Most occurring characters

ValueCountFrequency (%)
a 9901
21.2%
n 6675
14.3%
r 3413
 
7.3%
e 3413
 
7.3%
U 3325
 
7.1%
d 3262
 
7.0%
K 1720
 
3.7%
F 1719
 
3.7%
c 1719
 
3.7%
G 1694
 
3.6%
Other values (6) 9904
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9901
21.2%
n 6675
14.3%
r 3413
 
7.3%
e 3413
 
7.3%
U 3325
 
7.1%
d 3262
 
7.0%
K 1720
 
3.7%
F 1719
 
3.7%
c 1719
 
3.7%
G 1694
 
3.6%
Other values (6) 9904
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9901
21.2%
n 6675
14.3%
r 3413
 
7.3%
e 3413
 
7.3%
U 3325
 
7.1%
d 3262
 
7.0%
K 1720
 
3.7%
F 1719
 
3.7%
c 1719
 
3.7%
G 1694
 
3.6%
Other values (6) 9904
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9901
21.2%
n 6675
14.3%
r 3413
 
7.3%
e 3413
 
7.3%
U 3325
 
7.1%
d 3262
 
7.0%
K 1720
 
3.7%
F 1719
 
3.7%
c 1719
 
3.7%
G 1694
 
3.6%
Other values (6) 9904
21.2%

is_churned
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
7232 
1
2768 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7232
72.3%
1 2768
 
27.7%

Length

2025-04-14T22:22:05.808648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:05.868048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7232
72.3%
1 2768
 
27.7%

Most occurring characters

ValueCountFrequency (%)
0 7232
72.3%
1 2768
 
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7232
72.3%
1 2768
 
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7232
72.3%
1 2768
 
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7232
72.3%
1 2768
 
27.7%

interaction_score
Real number (ℝ)

High correlation  Zeros 

Distinct5433
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean465.18063
Minimum0
Maximum2497.2
Zeros1882
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:05.940310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1108.675
median323.6
Q3676.5
95-th percentile1455.96
Maximum2497.2
Range2497.2
Interquartile range (IQR)567.825

Descriptive statistics

Standard deviation470.22954
Coefficient of variation (CV)1.0108537
Kurtosis1.2204883
Mean465.18063
Median Absolute Deviation (MAD)263.5
Skewness1.2877668
Sum4651806.3
Variance221115.82
MonotonicityNot monotonic
2025-04-14T22:22:06.031301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1882
 
18.8%
760.2 7
 
0.1%
486 6
 
0.1%
336 6
 
0.1%
160.5 6
 
0.1%
856.5 6
 
0.1%
559.2 6
 
0.1%
171.8 6
 
0.1%
219.6 6
 
0.1%
222.9 6
 
0.1%
Other values (5423) 8063
80.6%
ValueCountFrequency (%)
0 1882
18.8%
3.1 1
 
< 0.1%
4.9 1
 
< 0.1%
6.1 1
 
< 0.1%
6.6 1
 
< 0.1%
6.6 1
 
< 0.1%
7.8 1
 
< 0.1%
8.7 1
 
< 0.1%
9 1
 
< 0.1%
9.1 1
 
< 0.1%
ValueCountFrequency (%)
2497.2 1
< 0.1%
2470.8 1
< 0.1%
2432.7 1
< 0.1%
2430.3 1
< 0.1%
2417.7 1
< 0.1%
2409 1
< 0.1%
2385.3 1
< 0.1%
2381.7 1
< 0.1%
2367.9 1
< 0.1%
2337.6 1
< 0.1%

tenure_weeks
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.9328
Minimum60
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:06.117381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile72
Q1108
median156
Q3208
95-th percentile244
Maximum256
Range196
Interquartile range (IQR)100

Descriptive statistics

Standard deviation56.263082
Coefficient of variation (CV)0.35624698
Kurtosis-1.1981751
Mean157.9328
Median Absolute Deviation (MAD)48
Skewness-2.4284123 × 10-5
Sum1579328
Variance3165.5344
MonotonicityDecreasing
2025-04-14T22:22:06.211183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 206
 
2.1%
96 206
 
2.1%
132 206
 
2.1%
172 206
 
2.1%
120 206
 
2.1%
184 206
 
2.1%
108 206
 
2.1%
196 206
 
2.1%
208 206
 
2.1%
148 206
 
2.1%
Other values (40) 7940
79.4%
ValueCountFrequency (%)
60 76
 
0.8%
64 205
2.1%
68 205
2.1%
72 206
2.1%
76 205
2.1%
80 205
2.1%
84 206
2.1%
88 205
2.1%
92 205
2.1%
96 206
2.1%
ValueCountFrequency (%)
256 69
 
0.7%
252 205
2.1%
248 206
2.1%
244 205
2.1%
240 205
2.1%
236 206
2.1%
232 205
2.1%
228 205
2.1%
224 205
2.1%
220 206
2.1%

logins_per_month
Real number (ℝ)

High correlation 

Distinct666
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34399327
Minimum0.033898305
Maximum1.5294118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:06.302373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.033898305
5-th percentile0.14035088
Q10.21568627
median0.29787234
Q30.425
95-th percentile0.70588235
Maximum1.5294118
Range1.4955135
Interquartile range (IQR)0.20931373

Descriptive statistics

Standard deviation0.17916411
Coefficient of variation (CV)0.52083609
Kurtosis1.9124101
Mean0.34399327
Median Absolute Deviation (MAD)0.097476497
Skewness1.3137972
Sum3439.9327
Variance0.032099779
MonotonicityNot monotonic
2025-04-14T22:22:06.399564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 189
 
1.9%
0.3333333333 182
 
1.8%
0.5 175
 
1.8%
0.2857142857 115
 
1.1%
0.2 115
 
1.1%
0.4 89
 
0.9%
0.2222222222 77
 
0.8%
0.3 71
 
0.7%
0.4285714286 69
 
0.7%
0.1666666667 69
 
0.7%
Other values (656) 8849
88.5%
ValueCountFrequency (%)
0.03389830508 1
< 0.1%
0.03703703704 1
< 0.1%
0.03773584906 1
< 0.1%
0.04 1
< 0.1%
0.04444444444 1
< 0.1%
0.04545454545 1
< 0.1%
0.04651162791 1
< 0.1%
0.046875 1
< 0.1%
0.04838709677 1
< 0.1%
0.04918032787 2
< 0.1%
ValueCountFrequency (%)
1.529411765 1
 
< 0.1%
1.375 1
 
< 0.1%
1.1875 2
 
< 0.1%
1.166666667 2
 
< 0.1%
1.125 1
 
< 0.1%
1.117647059 5
0.1%
1.111111111 1
 
< 0.1%
1.1 1
 
< 0.1%
1.0625 3
< 0.1%
1.058823529 6
0.1%

pca1
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.4106051 × 10-17
Minimum-3.212245
Maximum3.0882128
Zeros0
Zeros (%)0.0%
Negative5031
Negative (%)50.3%
Memory size78.2 KiB
2025-04-14T22:22:06.490993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.212245
5-th percentile-1.6932057
Q1-0.69899133
median-0.0098073366
Q30.71732449
95-th percentile1.6622538
Maximum3.0882128
Range6.3004578
Interquartile range (IQR)1.4163158

Descriptive statistics

Standard deviation1.0164853
Coefficient of variation (CV)-2.9803663 × 1016
Kurtosis-0.33381018
Mean-3.4106051 × 10-17
Median Absolute Deviation (MAD)0.70824153
Skewness-0.0083593366
Sum1.3429258 × 10-12
Variance1.0332423
MonotonicityNot monotonic
2025-04-14T22:22:06.579471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.162221022 1
 
< 0.1%
-0.2917646343 1
 
< 0.1%
0.3772702113 1
 
< 0.1%
-1.344342236 1
 
< 0.1%
2.081520257 1
 
< 0.1%
-0.531597789 1
 
< 0.1%
1.404157246 1
 
< 0.1%
-1.910233016 1
 
< 0.1%
0.4021978666 1
 
< 0.1%
-0.8433151351 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-3.212245013 1
< 0.1%
-3.209804707 1
< 0.1%
-3.168312684 1
< 0.1%
-2.975755099 1
< 0.1%
-2.959117678 1
< 0.1%
-2.920276776 1
< 0.1%
-2.891708642 1
< 0.1%
-2.850614647 1
< 0.1%
-2.839986067 1
< 0.1%
-2.803212072 1
< 0.1%
ValueCountFrequency (%)
3.08821282 1
< 0.1%
3.017399738 1
< 0.1%
2.93872192 1
< 0.1%
2.923914175 1
< 0.1%
2.869394975 1
< 0.1%
2.867073933 1
< 0.1%
2.861697879 1
< 0.1%
2.860922731 1
< 0.1%
2.835967107 1
< 0.1%
2.82405865 1
< 0.1%

pca2
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9790393 × 10-17
Minimum-3.4730558
Maximum5.3107875
Zeros0
Zeros (%)0.0%
Negative5154
Negative (%)51.5%
Memory size78.2 KiB
2025-04-14T22:22:06.668643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4730558
5-th percentile-1.5973883
Q1-0.69668216
median-0.037872413
Q30.65142555
95-th percentile1.7205051
Maximum5.3107875
Range8.7838433
Interquartile range (IQR)1.3481077

Descriptive statistics

Standard deviation1.0048299
Coefficient of variation (CV)2.5253079 × 1016
Kurtosis0.21827559
Mean3.9790393 × 10-17
Median Absolute Deviation (MAD)0.67280465
Skewness0.24759118
Sum1.5489832 × 10-12
Variance1.0096832
MonotonicityNot monotonic
2025-04-14T22:22:06.754856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6775319111 1
 
< 0.1%
-1.084872714 1
 
< 0.1%
0.2985205883 1
 
< 0.1%
-1.404634705 1
 
< 0.1%
0.4530018173 1
 
< 0.1%
0.03927232468 1
 
< 0.1%
0.677996349 1
 
< 0.1%
0.9027957627 1
 
< 0.1%
0.8563151917 1
 
< 0.1%
1.437660491 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-3.473055836 1
< 0.1%
-3.39411458 1
< 0.1%
-3.301761833 1
< 0.1%
-3.266506973 1
< 0.1%
-3.087416497 1
< 0.1%
-3.052847818 1
< 0.1%
-3.049084275 1
< 0.1%
-2.976987511 1
< 0.1%
-2.902341059 1
< 0.1%
-2.803720628 1
< 0.1%
ValueCountFrequency (%)
5.310787461 1
< 0.1%
4.283956591 1
< 0.1%
4.086911179 1
< 0.1%
4.058523019 1
< 0.1%
4.028848394 1
< 0.1%
3.817298035 1
< 0.1%
3.78955842 1
< 0.1%
3.760487191 1
< 0.1%
3.759087588 1
< 0.1%
3.643242273 1
< 0.1%

cluster
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
3054 
1
2983 
3
2160 
2
1803 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3054
30.5%
1 2983
29.8%
3 2160
21.6%
2 1803
18.0%

Length

2025-04-14T22:22:06.833210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:06.896505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3054
30.5%
1 2983
29.8%
3 2160
21.6%
2 1803
18.0%

Most occurring characters

ValueCountFrequency (%)
0 3054
30.5%
1 2983
29.8%
3 2160
21.6%
2 1803
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3054
30.5%
1 2983
29.8%
3 2160
21.6%
2 1803
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3054
30.5%
1 2983
29.8%
3 2160
21.6%
2 1803
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3054
30.5%
1 2983
29.8%
3 2160
21.6%
2 1803
18.0%

Interactions

2025-04-14T22:22:01.950490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.021865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.674973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.309367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.915247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.479346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.136938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.734679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.372177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:02.007060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.099066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.734859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.373645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.975840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.539857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.200575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.815469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.435363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:02.159252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.188856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.793870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.438301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.034952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.601882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.264344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.883348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.496373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:02.226271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.296912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.862631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.508724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.104695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.671394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.336067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.958089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.566902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:02.283510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.356352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.922397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.573521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.162341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.732698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.398908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.027064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.627448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:02.342702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.419300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.984912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.639842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.224563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.794504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.465121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.094428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.691817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:02.406205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.486406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.051781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.711562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.290641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.940496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.534800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.167180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.759143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:02.473536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.556368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.188981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.785464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.360846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.013115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.608483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.240221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.830672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:02.534744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:57.619335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.252678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:58.854910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:21:59.422831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.079086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:00.675803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.310529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:01.893621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-14T22:22:06.960267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
auto_renewclustercountrydiscount_usedfeature_usage_scoreinteraction_scoreis_churnedlogins_per_monthmonthly_feenum_logins_last_30dpayment_methodpca1pca2referral_channelsubscription_typesupport_ticketstenure_monthstenure_weeks
auto_renew1.0000.9820.0000.0000.0000.0190.0000.0220.0000.0170.0000.4270.2000.0000.0000.0000.0000.000
cluster0.9821.0000.0000.8290.3890.2200.0220.0210.0420.0000.0000.3830.1500.0000.0000.0000.0070.007
country0.0000.0001.0000.0000.0000.0000.0000.0080.0000.0000.0000.0050.0000.0000.0080.0000.0220.022
discount_used0.0000.8290.0001.0000.0000.0310.0030.0000.0170.0140.0000.5190.1060.0000.0000.0000.0000.000
feature_usage_score0.0000.3890.0000.0001.0000.4660.000-0.0030.0010.0000.022-0.083-0.2130.0180.000-0.0000.0060.006
interaction_score0.0190.2200.0000.0310.4661.0000.000-0.0080.603-0.0020.0000.4660.0320.0000.0000.0100.0080.008
is_churned0.0000.0220.0000.0030.0000.0001.0000.0000.0150.0000.0150.0080.0080.0000.0000.0000.0170.017
logins_per_month0.0220.0210.0080.000-0.003-0.0080.0001.0000.0150.5930.004-0.3630.6070.0000.000-0.002-0.781-0.781
monthly_fee0.0000.0420.0000.0170.0010.6030.0150.0151.0000.0000.0000.4530.1170.0000.0000.0070.0270.027
num_logins_last_30d0.0170.0000.0000.0140.000-0.0020.0000.5930.0001.0000.000-0.2550.6240.0000.0100.003-0.007-0.007
payment_method0.0000.0000.0000.0000.0220.0000.0150.0040.0000.0001.0000.0080.0000.0000.0060.0000.0000.000
pca10.4270.3830.0050.519-0.0830.4660.008-0.3630.453-0.2550.0081.0000.0040.0000.0000.2500.2640.264
pca20.2000.1500.0000.106-0.2130.0320.0080.6070.1170.6240.0000.0041.0000.0250.0130.591-0.296-0.296
referral_channel0.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0251.0000.0090.0130.0000.000
subscription_type0.0000.0000.0080.0000.0000.0000.0000.0000.0000.0100.0060.0000.0130.0091.0000.0000.0000.000
support_tickets0.0000.0000.0000.000-0.0000.0100.000-0.0020.0070.0030.0000.2500.5910.0130.0001.0000.0040.004
tenure_months0.0000.0070.0220.0000.0060.0080.017-0.7810.027-0.0070.0000.264-0.2960.0000.0000.0041.0001.000
tenure_weeks0.0000.0070.0220.0000.0060.0080.017-0.7810.027-0.0070.0000.264-0.2960.0000.0000.0041.0001.000

Missing values

2025-04-14T22:22:02.634951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T22:22:02.836740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idsignup_datelast_active_datetenure_monthssubscription_typemonthly_feeauto_renewpayment_methodnum_logins_last_30dsupport_ticketsfeature_usage_scorediscount_usedreferral_channelcountryis_churnedinteraction_scoretenure_weekslogins_per_monthpca1pca2cluster
0CUST000002019-01-01 00:00:00.0000000002019-05-13 00:00:00.00000000064Basic01Wallet11226.611OrganicFrance00.02560.169231-1.162221-0.6775323
1CUST000012019-01-01 03:30:24.3024302432020-04-10 03:30:24.30243024364Premium300PayPal13233.551ReferralFrance01006.52560.2000001.052944-0.4309311
2CUST000022019-01-01 07:00:48.6048604862021-06-09 07:00:48.60486048664Premium01Wallet18517.550PartnerCanada00.02560.2769230.0607432.4706450
3CUST000032019-01-01 10:31:12.9072907292019-10-28 10:31:12.90729072964Premium301Other9226.070OrganicUK0782.12560.1384621.573729-0.4075450
4CUST000042019-01-01 14:01:37.2097209722019-05-17 14:01:37.20972097264Premium101Other1746.420PartnerIndia064.22560.2615380.4876252.0751340
5CUST000052019-01-01 17:32:01.5121512152019-04-12 17:32:01.51215121564Premium101Other15222.441PartnerUK1224.42560.230769-0.9416360.2498153
6CUST000062019-01-01 21:02:25.8145814582020-12-31 21:02:25.81458145864Free00Wallet15055.411PartnerUS00.02560.230769-1.168066-1.7623531
7CUST000072019-01-02 00:32:50.1170117012019-02-21 00:32:50.11701170164Basic101PayPal9124.710PaidFrance1247.12560.1384620.354563-1.1516210
8CUST000082019-01-02 04:03:14.4194419442020-10-07 04:03:14.41944194464Basic300PayPal14036.181ReferralUK01085.42560.2153850.529641-1.2814691
9CUST000092019-01-02 07:33:38.7218721872019-06-02 07:33:38.72187218764Premium01Wallet9320.791OrganicUS00.02560.138462-0.760670-0.4656143
customer_idsignup_datelast_active_datetenure_monthssubscription_typemonthly_feeauto_renewpayment_methodnum_logins_last_30dsupport_ticketsfeature_usage_scorediscount_usedreferral_channelcountryis_churnedinteraction_scoretenure_weekslogins_per_monthpca1pca2cluster
9990CUST099902022-12-30 16:26:21.2781278082024-06-08 16:26:21.27812780815Premium301Wallet18325.940OrganicIndia0778.2601.12500.1598132.7875060
9991CUST099912022-12-30 19:56:45.5805580482024-03-06 19:56:45.58055804815Basic301Other13261.930OrganicGermany01857.9600.81250.1315750.8638982
9992CUST099922022-12-30 23:27:09.8829882882023-12-17 23:27:09.88298828815Basic100PayPal14440.610ReferralUS0406.1600.87500.4836141.6592751
9993CUST099932022-12-31 02:57:34.1854185282023-08-27 02:57:34.18541852815Basic100Other7135.760PartnerFrance1357.6600.43750.400046-1.0595731
9994CUST099942022-12-31 06:27:58.4878487842024-10-05 06:27:58.48784878415Premium301PayPal8014.361PaidIndia0430.8600.5000-0.719845-0.6247013
9995CUST099952022-12-31 09:58:22.7902790242023-12-24 09:58:22.79027902415Premium100Other9223.751PartnerUS0237.5600.5625-0.535503-0.2588711
9996CUST099962022-12-31 13:28:47.0927092642024-02-05 13:28:47.09270926415Premium100Other17236.790PartnerFrance0367.9601.0625-0.1582001.2674401
9997CUST099972022-12-31 16:59:11.3951395042024-05-03 16:59:11.39513950415Premium300Card9237.351PaidUS11120.5600.56250.400786-0.1849961
9998CUST099982022-12-31 20:29:35.6975697442023-06-11 20:29:35.69756974415Basic101Card10218.011ReferralIndia0180.1600.6250-1.4719870.4266223
9999CUST099992023-01-01 00:00:00.0000000002025-05-14 00:00:00.00000000015Basic00Other11140.740PartnerCanada00.0600.6875-0.439889-0.5243111